PhD researcher: Control and task planning for advanced manufacturing with domain knowledge and expert feedback (100 %)

School: School of Engineering
Starting date: January 2025 or by mutual appointment

Your role

Advanced manufacturing refers to a set of technologies and methods incorporated in manufacturing with the goal to improve its adaptivity and sustainability, to achieve the flexibility needed for the production of individual parts. Still, the challenges of standard manufacturing remain. Methods for improving adaptivity in the presence of drifts and different production scenarios are of high importance in achieving the goals of advanced manufacturing. Our research aims to develop novel methods at the intersection of data-driven optimization, manufacturing science, and machine learning, to improve adaptivity by incorporating robustly expert feedback and preferences. 

We are looking for a motivated PhD student to contribute to this effort. We will explore methods to incorporate preferences based on reinforcement learning and data-driven optimization, as well as on constrained optimization. Furthermore, the connection between expert preferences in text form and underlying control actions will be studied. The methods will be demonstrated on robotic systems available in our lab. 

Your goal will be to translate your own research ideas to tackle these challenges, in close collaboration with our interdisciplinary team. As part of this process, you will support our bachelor, and master students, publish in scientific journals, contribute to our teaching efforts, and participate in conferences. You will closely communicate with academic and industry partners. 

The position is supported by NCCR Automation and offers excellent opportunities for national and international collaboration with academic and industrial partners. The position offers a good opportunity for the development of coordination and project management skills, PhD student supervision, and leadership skills.

The position is hosted at the ZHAW Centre for AI, jointly supervised by Dr. Alisa Rupenyan (endowed professorship for Industrial AI at ZHAW Centre for AI), Prof. Dr. John Lygeros (heading the Automatic Control Laboratory at ETH Zurich), and Dr. Efe Balta (group leader for Advanced control and automation at inspire AG).
ETH Zurich rules for doctoral admission apply.

Your profile

You are highly motivated and dedicated with a recognized master degree in electrical, mechanical or industrial engineering, or machine learning/AI with exposure to control systems/theory, and a focus on optimization, data-driven methods for optimization and control, or probabilistic AI. You have some experience in setting up or working with robotics/autonomous systems in research labs. Programming, modelling, and data analysis skills in Python, C++, and ROS will support you in contributing to our ongoing software development efforts. You are autonomous and interested in participating and managing large-scale collaboration projects. We expect fluent English knowledge, and German could be beneficial.

This is what we stand for

Zurich University of Applied Sciences ZHAW is one of Switzerland's largest multidisciplinary universities of applied sciences, with over 14'000 students and 3'400 faculty and staff.

The School of Engineering (SoE), as one of the leading edu-cational and research institutions in Switzerland, focuses on topics relevant to the future. 14 institutes and centres guarantee high-quality education, research and development with a focus on the areas of energy, mobility, information and health.

The Centre for Artificial Intelligence (CAI) is a Center of Excellence at ZHAW and maintains a growing number of research groups in various domains. The Industrial AI group (https://www.zhaw.ch/en/engineering/institutes-centres/cai/industrial-artificial-intelligence-group/) specializes in research for the improvement and automa-tion of complex manufacturing systems, combining methods from robotics, AI, and control systems.

We are looking for a deeply interested, technically skilled personality. Please honor us with a tailored cover letter and highlight facts that underline your suitability for this position, transcripts of grades from (some of) your degrees, and 2 reference contacts. We are looking forward to hearing from you!

ZHAW is committed to gender-mixed and diverse teams in order to promote equality, diversity and innovation.

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